A State-Space Approach for Detecting Stress from Electrodermal Activity

Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress, Wellness, and Anxiety: A Comprehensive Review

The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets and portable techniques utilized for diagnosing depression, stress, and emotional trauma while also exploring the underlying biochemical processes associated with their identification. Integrating various detectors within smartphones and smart bands enables continuous monitoring and recording of user activities. Given their widespread use, smartphones, smartwatches, and smart wristbands have become indispensable in our daily lives, prompting the exploration of their potential in stress detection and prevention. When individuals experience stress, their nervous system responds by releasing stress hormones, which can be easily identified and quantified by smartphones and smart bands. The study in this paper focused on the examination of anxiety and stress and consistently employed "heart rate variability" (HRV) characteristics for diagnostic purposes, with superior outcomes observed when HRV was combined with "electroencephalogram" (EEG) analysis. Recent research indicates that electrodermal activity (EDA) demonstrates remarkable precision in identifying anxiety. Comparisons with HRV, EDA, and breathing rate reveal that the mean heart rate employed by several commercial wearable products is less accurate in identifying anxiety and stress. This comprehensive review article provides an evidence-based evaluation of intelligent gadgets and wearable sensors, highlighting their potential to accurately assess stress, wellness, and anxiety. It also identifies areas for further research and development.

Eye tracking is more sensitive than skin conductance response in detecting mild environmental stimuli

The skin conductance (SC) and eye tracking data are two potential arousal-related psychophysiological signals that can serve as the interoceptive unconditioned response to aversive stimuli (e.g. electric shocks). The current research investigates the sensitivity of these signals in detecting mild electric shock by decoding the hidden arousal and interoceptive awareness (IA) states. While well-established frameworks exist to decode the arousal state from the SC signal, there is a lack of a systematic approach that decodes the IA state from pupillometry and eye gaze measurements. We extract the physiological-based features from eye tracking data to recover the IA-related neural activity. Employing a Bayesian filtering framework, we decode the IA state in fear conditioning and extinction experiments where mild electric shock is used. We independently decode the underlying arousal state using binary and marked point process (MPP) observations derived from concurrently collected SC data. Eight of 11 subjects present a significantly (P-value < 0.001 ) higher IA state in trials that were always accompanied by electric shock ( CS + US + ) compared to trials that were never accompanied by electric shock ( CS - ). According to the decoded SC-based arousal state, only five (binary observation) and four (MPP observation) subjects present a significantly higher arousal state in CS + US + trials than CS - trials. In conclusion, the decoded hidden brain state from eye tracking data better agrees with the presented mild stimuli. Tracking IA state from eye tracking data can lead to the development of contactless monitors for neuropsychiatric and neurodegenerative disorders.

A multimodal dataset for investigating working memory in presence of music: a pilot study

Introduction

Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.

Methods

Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.

Results

A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.

Discussion

Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work

Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.

Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examined the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual's baseline biosignal patterns, thus enabling personalization with very few labels, we pre-trained a one-dimensional convolutional neural network (1D CNN) using self-supervised learning (SSL). We evaluated our method using the Wearable Stress and Affect Detection(WESAD) dataset. We fine-tuned the pre-trained networks to the stress-prediction task and compared against equivalent models without any self-supervised pre-training. We discovered that embeddings learned using our pre-training method outperformed the supervised baselines with significantly fewer labeled data points: the models trained with SSL required less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems that are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.

Characterizing Alterations in Cortisol Secretion During Cardiac Surgery

Cortisol is a neuroendocrine hormone of the hypothalamus-pituitary-adrenal (HPA) axis secreted from adrenal glands in response to stimulation by adrenocorticotropic hormone (ACTH) from the anterior pituitary and corticotropin releasing hormone (CRH) from the hypothalamus. Cortisol has multiple functionalities in maintaining bodily homeostasis - including anti-inflammatory influences - through its diurnal secretion pattern (which has been studied extensively); its secretion is also increased in response to major traumatic events such as surgery. Due to the adverse health consequences of an abnormal immune response, it is crucial to understand the effect of cortisol in modulating inflammation. To address this physiological issue, we characterize the secretion of cortisol using a high temporal resolution dataset of ten patients undergoing coronary arterial bypass grafting (CABG) surgery, in comparison with a control group not undergoing surgery. We find that cortisol exhibits different pulsatile dynamics in those undergoing cardiac surgery compared to the control subjects. We also summarize the causality of cortisol's relationship with different cytokines (which are one type of inflammatory markers) by performing Granger causality analysis.Clinical relevance- This work documents time-varying patterns of the HPA axis hormone cortisol in the inflammatory response to cardiac surgery and may eventually help improve patients' prognosis post-surgery (or in other conditions) by enabling early detection of an abnormal cortisol or inflammatory response and enabling patient specific remedial interventions.

A Point-Process Approach for Tracking Valence using a Respiration Belt

Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.

Characterization of Leptin Secretion in Premenopausal Obese Women Treated with Bromocriptine

Leptin, a hormone secreted by adipose tissue, is primarily responsible for inhibiting hunger and maintaining energy balance. Improper leptin secretion may result in hyperleptinemia (excess secretion of leptin) or leptin resistance, both of which contribute to obesity. Diagnosing abnormal leptin secretion may help treat this underlying cause of obesity. Therefore, continuous monitoring of the level of leptin may help characterize its secretion dynamics and also help devise an appropriate treatment. In this research, we consider leptin hormone concentration data taken over a 24 hour time period from eighteen healthy premenopausal obese women before and after treatment with a dopamine agonist, bromocriptine, and deconvolve the observed leptin hormone levels to estimate the number, timing, and magnitude of the underlying leptin secretory pulses. We find that there is an overall decrease in leptin secretion, particularly during sleep, but the changes in the secretory and clearance rates, and the number of pulses underlying the secretion process are not statistically significant.Clinical relevance- This work seeks to understand the effect of bromocriptine on leptin secretory dynamics and will help further current understanding of the effect of bromocriptine in relation to obesity.

Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.

Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures

Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

A State-space Investigation of Impact of Music on Cognitive Performance during a Working Memory Experiment

Stress has effects on productivity and performance. Poor stress management may lead to reduced productivity and performance. Non-invasive actuators such as music have the potential to effectively regulate stress. In this study, using a state-space approach, we obtain a performance state to investigate the performance during a working memory task while playing two different types of music in the background. In our experiments, participants performed a working memory task while listening to calming and vexing music of their choice. We utilize the binary correct/incorrect response and the continuous reaction time of the response from the participants to quantify the performance. The state-space quantification reveals that vexing music has a statistically significant positive impact on the obtained performance state. This indicates the feasibility of designing non-invasive closed-loop systems to regulate stress for maximizing performance and productivity.

Decoding a Music-Modulated Cognitive Arousal State using Electrodermal Activity and Functional Near-infrared Spectroscopy Measurements

Biofeedback systems sense different physiological activities and help with gaining self-awareness. Understanding music's impact on the arousal state is of great importance for biofeedback stress management systems. In this study, we investigate a cognitive-stress-related arousal state modulated by different types of music. During our experiments, each subject was presented with neurological stimuli that elicit a cognitive-stress-related arousal response in a working memory experiment. Moreover, this cognitive-stress-related arousal was modulated by calming and vexing music played in the background. Electrodermal activity and functional near-infrared spectroscopy (fNIRS) measurements both contain information related to cognitive arousal and were collected in our study. By considering various fNIRS features, we selected three features based on variance, root mean square, and local fNIRS peaks as the most informative fNIRS observations in terms of cognitive arousal. The rate of neural impulse occurrence underlying EDA was taken as a binary observation. To retain a low computational complexity for our decoder and select the best fNIRS-based observations, two features were chosen as fNIRS-based observations at a time. A decoder based on one binary and two continuous observations was utilized to estimate the hidden cognitive-stress-related arousal state. This was done by using a Bayesian filtering approach within an expectation-maximization framework. Our results indicate that the decoded cognitive arousal modulated by vexing music was higher than calming music. Among the three fNIRS observations selected, a combination of observations based on root mean square and local fNIRS peaks resulted in the best decoded states for our experimental settings. This study serves as a proof of concept for utilizing fNIRS and EDA measurements to develop a low-dimensional decoder for tracking cognitive-stress-related arousal levels.

Decoding a Neurofeedback-Modulated Cognitive Arousal State to Investigate Performance Regulation by the Yerkes-Dodson Law

Enhancing the productivity of humans by regulating arousal during cognitive tasks is a challenging topic in psychology that has a great potential to transform workplaces for increased productivity and educational systems for enhanced performance. In this study, we assess the feasibility of using the Yerkes-Dodson law from psychology to improve performance during a working memory experiment. We employ a Bayesian filtering approach to track cognitive arousal and performance. In particular, by utilizing skin conductance signal recorded during a working memory experiment in the presence of music, we decode a cognitive arousal state. This is done by considering the rate of neural impulse occurrences and their amplitudes as observations for the arousal model. Similarly, we decode a performance state using the number of correct and incorrect responses, and the reaction time as binary and continuous behavioral observations, respectively. We estimate the arousal and performance states within an expectation-maximization framework. Thereafter, we design an arousal-performance model on the basis of the Yerkes-Dodson law and estimate the model parameters via regression analysis. In this experiment musical neurofeedback was used to modulate cognitive arousal. Our investigations indicate that music can be used as a mode of actuation to influence arousal and enhance the cognitive performance during working memory tasks. Our findings can have a significant impact on designing future smart workplaces and online educational systems.

A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data

Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.

Electrodermal activity (EDA) is an indirect read-out of the body’s sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show that a physiologically-based statistical model provides a parsimonious and accurate description of EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Color-dependent changes in humans during a verbal fluency task under colored light exposure assessed by SPA-fNIRS

Light evokes robust visual and nonvisual physiological and psychological effects in humans, such as emotional and behavioral responses, as well as changes in cognitive brain activity and performance. The aim of this study was to investigate how colored light exposure (CLE) and a verbal fluency task (VFT) interact and affect cerebral hemodynamics, oxygenation, and systemic physiology as determined by systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). 32 healthy adults (17 female, 15 male, age: 25.5 ± 4.3 years) were exposed to blue and red light for 9 min while performing a VFT. Before and after the CLE, subjects were in darkness. We found that this long-term CLE-VFT paradigm elicited distinct changes in the prefrontal cortex and in most systemic physiological parameters. The subjects' performance depended significantly on the type of VFT and the sex of the subject. Compared to red light, blue evoked stronger responses in cerebral hemodynamics and oxygenation in the visual cortex. Color-dependent changes were evident in the recovery phase of several systemic physiological parameters. This study showed that the CLE has effects that endure at least 15 min after cessation of the CLE. This underlines the importance of considering the persistent influence of colored light on brain function, cognition, and systemic physiology in everyday life.

Pain and Stress Detection Using Wearable Sensors and Devices-A Review

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

OBJECTIVE

Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal.

METHODS

We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual.

RESULTS

We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with ). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component.

CONCLUSION

Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution.

SIGNIFICANCE

Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.

Olfactory-induced Positive Affect and Autonomic Response as a Function of Hedonic and Intensity Attributes of Fragrances

Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophysiology. Despite advances in olfactory-affective neuroscience, it is unclear how separate attributes of odor stimuli contribute to olfactoryinduced emotions, especially within the positive segment of the hedonic dimension to avoid potential cross-valence confounds. In this study, we examined how pleasantness and intensity of fragrances relate to different grades of positive affect. Our results show that greater odor pleasantness and intensity are independently associated with stronger positive affect. Pleasantness has a greater influence than intensity in evoking a positive vs. neutral affect, whereas intensity is more impactful than pleasantness in evoking an extreme positive vs. positive response. Autonomic response, as assessed by the galvanic skin response (GSR) was found to decrease with increasing pleasantness but not intensity. This clarifies how olfactory and affective processing induce significant downstream effects in peripheral physiology and self-reported affective experience, pertinent to the thriving field of olfactory neuromarkerting.

A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning

Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.

Tonic and Phasic Decomposition of Skin Conductance Data: A Generalized-Cross-Validation-Based Block Coordinate Descent Approach

Salty sweat secretions in the epidermis change the skin's electrical activity resulting in the measured skin conductance signal. While the relatively fast variation of skin conductance (i.e. phasic component) reflects sympathetic nervous system activity, the slow variation (i. e. tonic component) is related to thermoregulation and general arousal. To better understand the neural information encoded in a skin conductance signal, it is necessary to decompose it into its constituent components. We model the fast variations using a second order differential equation incorporating a sparse impulsive input to the model. Furthermore, we model the tonic component with several cubic basis spline functions. Finally, we develop a block coordinate descent approach for skin conductance signal decomposition by employing generalized-cross-validation for balancing between smoothness of the tonic component, the sparsity of the neural stimuli, and residual error. We analyze experimental and simulated data to validate the performance of the proposed approach. We successfully illustrate its ability to recover the neural stimuli, the underlying physiological system parameters, and both tonic and phasic components. In summary, we develop a novel approach for decomposition of phasic and tonic components of skin conductance signal using a generalized-cross-validation-based block coordinate descent approach. Recovering the underlying neural stimuli and the tonic component accurately could potentially improve cognitive-stress-related arousal states estimation for better stress regulation in mental health disorders.

A Novel Filter for Tracking Real-World Cognitive Stress using Multi-Time-Scale Point Process Observations

Determining the relationship between neurocognitive stress and changes in physiological signals is an important aspect of wearable monitoring. We present a state-space approach for tracking stress from skin conductance and electrocardiography measurements. Individual skin conductance responses (SCRs) are a primary source of information in a skin conductance signal and their rate of occurrence is related to psychological arousal. Likewise, heart rate too varies with emotion. We model SCRs and heartbeats as two different stress-related point processes linked to the same sympathetic nervous system activation. We derive Kalman-like filter equations for tracking stress and use both expectation-maximization and maximum likelihood estimation for parameter recovery. Our preliminary results show that stress is high when a task is unfamiliar, but reduces gradually with familiarity, albeit in the presence of other external stressors. The method demonstrates the feasibility of tracking real-world stress using skin conductance and heart rate measurements. It also serves as a novel state estimation framework for multiple point process observations on different time scales.

State-Space Modeling and Fuzzy Feedback Control of Cognitive Stress

"Distress" or a substantial amount of stress may decrease brain functionality and cause neurological disorders. On the other hand, very low cognitive arousal may affect one's concentration and awareness. Data collected using wrist-worn wearable devices, in particular, skin conductance data, could be used to look into one's cognitive-stress-related arousal. Our goal here is to present excitatory and inhibitory wearable machine-interface (WMI) architectures to control one's cognitive-stress-related arousal state. We first present a model for skin conductance response events as a function of environmental stimuli associated with cognitive stress and relaxation. Then, we perform Bayesian filtering to estimate the hidden cognitive-stress-related arousal state. We finally close the loop using fuzzy control. In particular, we design two classes of controllers for our WMI architectures: (1) an inhibitory controller for reducing arousal and (2) an excitatory controller for increasing arousal. Our results illustrate that our simulated skin conductance responses are in agreement with experimental data. Moreover, we illustrate that our fuzzy control can successfully have both inhibitory and excitatory effects and regulate one's cognitive stress. In conclusion, in a simulation study based on experimental data, we have illustrated the feasibility of designing both excitatory and inhibitory WMI architectures. Since wearable devices can be used conveniently in one's daily life, the WMI architectures have a great potential to regulate one's cognitive stress seamlessly in real-world situations.

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

A Bayesian Filtering Approach for Tracking Arousal From Binary and Continuous Skin Conductance Features

OBJECTIVE

Neuroanatomical structures within the cortical and sub-cortical brain regions process emotion and cause subsequent variations in signals such as skin conductance and electrocardiography. The signals often encode information in their continuous-valued amplitudes or waves as well as in their underlying impulsive events. We propose to track psychological arousal from this hybrid source of skin conductance information.

METHODS

We present a point process state-space method in tandem with Bayesian filtering for determining a continuous-valued arousal state from skin conductance measurements. To perform state estimation, we relate arousal to binary- and continuous-valued observations derived from the phasic and tonic parts of a skin conductance signal, and recover model parameters using expectation-maximization. We evaluate our model on both synthetic and two different experimental data sets. Stress was artificially induced in the first experimental data set and the second comprised of a fear conditioning experiment.

RESULTS

Results on the first data set indicate high levels of arousal during exposure to cognitive stress and low arousal during relaxation. Plausible results are also obtained in the fear conditioning data set consistent with previous skin conductance studies in similar experimental contexts.

CONCLUSION

The state-space approach-which does not rely on external classification labels-is able to continuously track an arousal level from skin conductance features.

SIGNIFICANCE

The method is a promising arousal estimation scheme utilizing only skin conductance. The approach could find applications in wearable monitoring and the study of neuropsychiatric conditions such as post-traumatic stress disorder.

Skin Conductance as a Viable Alternative for Closing the Deep Brain Stimulation Loop in Neuropsychiatric Disorders

Markers from local field potentials, neurochemicals, skin conductance, and hormone concentrations have been proposed as a means of closing the loop in Deep Brain Stimulation (DBS) therapy for treating neuropsychiatric and movement disorders. Developing a closed-loop DBS controller based on peripheral signals would require: (i) the recovery of a biomarker from the source neural stimuli underlying the peripheral signal variations; (ii) the estimation of an unobserved brain or central nervous system related state variable from the biomarker. The state variable is application-specific. It is emotion-related in the case of depression or post-traumatic stress disorder, and movement-related for Parkinson's or essential tremor. We present a method for closing the DBS loop in neuropsychiatric disorders based on the estimation of sympathetic arousal from skin conductance measurements. We deconvolve skin conductance via an optimization formulation utilizing sparse recovery and obtain neural impulses from sympathetic nerve fibers stimulating the sweat glands. We perform this deconvolution via a two-step coordinate descent procedure that recovers the sparse neural stimuli and estimates physiological system parameters simultaneously. We next relate an unobserved sympathetic arousal state to the probability that these neural impulses occur and use Bayesian filtering within an Expectation-Maximization framework for estimation. We evaluate our method on a publicly available data-set examining the effect of different types of stress on peripheral signal changes including body temperature, skin conductance and heart rate. A high degree of arousal is estimated during cognitive tasks, as are much lower levels during relaxation. The results demonstrate the ability to decode psychological arousal from neural activity underlying skin conductance signal variations. The complete pipeline from recovering neural stimuli to decoding an emotion-related brain state using skin conductance presents a promising methodology for the ultimate realization of a closed-loop DBS controller. Closed-loop DBS treatment would additionally help reduce unnecessary power consumption and improve therapeutic gains.

Sparse Deconvolution of Electrodermal Activity via Continuous-Time System Identification

OBJECTIVE

Electrodermal activity (EDA) indicates different eccrine sweat gland activity caused by the stimulation of the autonomic nervous system. Recovering the number, timings, and amplitudes of underlying neural stimuli and physiological system parameters from the EDA is a challenging problem. One of the challenges with the existing methods is the non-convexity of the optimization formulations for estimating the parameters given the stimuli.

METHODS

We solve this parameter estimation problem using the following continuous-time system identification framework: 1) we specifically use the Hartley modulating function (HMF) for parameter estimation so that the optimization formulation for estimating the parameters given the stimuli is convex; and 2) we use Kaiser windows with different shape parameters to put more emphasis on the significant spectral components so that there is a balance between filtering out the noise and capturing the data. We apply this algorithm to skin conductance (SC) data, a measure of EDA, collected during cognitive stress experiments.

RESULTS

Under a sparsity constraint, in the HMF domain, we successfully deconvolve the SC signal. We obtain number, timings, and amplitudes of the underlying neural stimuli along with the system parameters with R² above 0.915. Moreover, using simulated data, we illustrate that our approach outperforms the existing EDA data analysis methods, in recovering underlying stimuli.

CONCLUSION

We develop a novel approach for deconvolution of SC by employing the HMF method and capturing the significant spectral components of SC data.

SIGNIFICANCE

Recovering the underlying neural stimuli more accurately using this approach will potentially improve tracking emotional states in affective computing.